Range

the centaur lesson remains: the more a task shifts to an open world of big-picture strategy, the more humans have to add.

It took Treffert decades to realize he had been wrong, and that
savants have more in common with prodigies like the Polgar sisters than he thought. They do not merely regurgitate. Their brilliance, just like the Polgar brilliance, relies on repetitive structures, which is precisely what made the Polgars’ skill so easy to automate.

We all rely on chunking every day in skills in which we are expert.

Studying an enormous number of repetitive patterns is so important in chess that early specialization in technical practice is critical.

The reason that elite athletes seem to have superhuman
reflexes is that they recognize patterns of ball or body movements that tell them what’s coming before it happens.

Chunking helps explain instances of apparently miraculous, domain-specific memory,

Moravec’s paradox: machines and humans frequently have opposite strengths and weaknesses.

“Anything we can do, and we know how to do it, machines will do it better,” he said at a recent lecture. “If we can codify it, and pass it to computers, they will do it better.”

In the most devilishly wicked learning environments, experience will reinforce the exact wrong lessons.

Kahneman was focused on the flip side of
kind learning environments; Hogarth called them “wicked.”

In wicked domains, the rules of the game are often unclear or incomplete, there may or may not be repetitive patterns and they may not be obvious, and feedback is often delayed, inaccurate, or both.